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Adds moments for inverse gamma distribution #5199

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Merged
merged 2 commits into from
Nov 18, 2021
Merged

Adds moments for inverse gamma distribution #5199

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ae87de6
adds moments for inverse gamma distribution, returns the mode when th…
morganstrom Nov 17, 2021
414ff4e
Remove redundant test case
morganstrom Nov 18, 2021
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20 changes: 8 additions & 12 deletions pymc/distributions/continuous.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2334,23 +2334,19 @@ def dist(cls, alpha=None, beta=None, mu=None, sigma=None, sd=None, *args, **kwar
alpha = at.as_tensor_variable(floatX(alpha))
beta = at.as_tensor_variable(floatX(beta))

# m = beta / (alpha - 1.0)
# try:
# mean = (alpha > 1) * m or np.inf
# except ValueError: # alpha is an array
# m[alpha <= 1] = np.inf
# mean = m

# mode = beta / (alpha + 1.0)
# variance = at.switch(
# at.gt(alpha, 2), (beta ** 2) / ((alpha - 2) * (alpha - 1.0) ** 2), np.inf
# )

assert_negative_support(alpha, "alpha", "InverseGamma")
assert_negative_support(beta, "beta", "InverseGamma")

return super().dist([alpha, beta], **kwargs)

def get_moment(rv, size, alpha, beta):
mean = beta / (alpha - 1.0)
mode = beta / (alpha + 1.0)
moment = at.switch(alpha > 1, mean, mode)
if not rv_size_is_none(size):
moment = at.full(size, moment)
return moment

@classmethod
def _get_alpha_beta(cls, alpha, beta, mu, sigma):
if alpha is not None:
Expand Down
16 changes: 16 additions & 0 deletions pymc/tests/test_distributions_moments.py
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Expand Up @@ -31,6 +31,7 @@
HalfNormal,
HalfStudentT,
HyperGeometric,
InverseGamma,
Kumaraswamy,
Laplace,
Logistic,
Expand Down Expand Up @@ -396,6 +397,21 @@ def test_gamma_moment(alpha, beta, size, expected):
assert_moment_is_expected(model, expected)


@pytest.mark.parametrize(
"alpha, beta, size, expected",
[
(5, 1, None, 1 / 4),
(0.5, 1, None, 1 / 1.5),
(5, 1, 5, np.full(5, 1 / (5 - 1))),
(np.arange(1, 6), 1, None, np.array([0.5, 1, 1 / 2, 1 / 3, 1 / 4])),
],
)
def test_inverse_gamma_moment(alpha, beta, size, expected):
with Model() as model:
InverseGamma("x", alpha=alpha, beta=beta, size=size)
assert_moment_is_expected(model, expected)


@pytest.mark.parametrize(
"alpha, m, size, expected",
[
Expand Down